Sequential recommendation.
Sequential recommendation [1]}, [2]}, [3]} aims to predict
the next interacted items based on historical interaction sequences.
Early works follow the Markov Chain assumption to estimate item-item transition relations based on matrix factorization [1]}. With the rapid development of deep neural networks,
numerous studies have been focusing on designing different sequential network models, including Recurrent Neural Network (RNN) [2]}, [6]}, Convolutional Neural Network (CNN) [7]}, Transformer [3]}, [9]}, Graph Neural Network (GNN) [10]}, [11]} and even Multilayer Perceptron (MLP) [12]}. However, most of these approaches are developed based on
item IDs [3]} or attributes [14]} defined on one specific domain, making it difficult to leverage behavior sequences from other domains or platforms. More recently, there have been several attempts that employ textual or visual features as transferable item representations [15]}, [16]}, [17]}.
Furthermore, several studies propose to construct a unified user model to solve multiple recommendation-oriented tasks
based on PLMs [18]}, [19]}.
Our work is built on these studies, while has a different focus: we incorporate discrete codes to decouple the binding between text encodings and item representations, and enhance the representation capacity with specially designed pre-training and fine-tuning strategies.
